AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease using Novel Non-invasive Biomarkers

• Published in: IEEE journal of biomedical and health informatics Vol. PP; pp. 1 - 9
• Main Authors: Sajid, Muhammad, Hassan, Ali, Khan, Dilshad Ahmed, Khan, Shoab Ahmed, Bakhshi, Asim Dilawar, Akram, Muhammad Usman, Babar, Mishal, Hussain, Farhan, Abdul, Wadood
• Format: Journal Article
• Language: English
• Published: United States IEEE 03-09-2024
• Subjects: Angiography; Arteries; Biological system modeling; Biomarkers; CAD Severity; Coronary Artery Disease; Gensini Score; Heart; Risk management; Risk Stratification; Solid modeling; Stenosis Percentage
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3453911

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography. Novel clinical, chemical, and molecular cardiac biomarkers were used as input features from an especially collected dataset. Following a thorough evaluative search in the biomarker feature space, the optimum feature and classifier or regression technique (regressor) set were selected using K-fold cross-validation. Ten machine learning (ML) classifiers were employed in classification tasks to determine the number of affected cardiac vessels, the Gensini group, and the severity of CAD with 82.58%, 86.26%, and 90.91% accuracy, respectively. Eleven approaches were used in regression tasks to calculate stenosis percentage and Gensini score, with R-squared values of 0.58 and 0.56, respectively. Following a thorough evaluative search in the biomarkers feature space, the optimum feature and classifier or regressor set were selected using K-fold cross-validation. The biomarkers and classifier or regressor combinations serve as the foundation for the proposed risk stratification framework, incorporating clinical protocol. Finally, our proposed framework is compared to state-of-the-art studies, offering a robust, well-rounded, early detection capable, and novel 'biomarkers-ML combination' approach to risk stratification.